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The present invention relates to methods of employing a metal-organic framework (MOF) as a catalyst for cleaving chemical bonds. In particular instances, the MOF results in selective bond cleavage that results in hydrogenolyzis. Furthermore, the MOF catalyst can be reused in multiple cycles. Such MOF-based catalysts can be useful, e.g., to convert biomass components.
Understanding ion transport in porous carbons is critical for a wide range of technologies, including supercapacitors and capacitive deionization for water desalination, yet many details remain poorly understood. For instance, an atomistic understanding of how ion selectivity is influenced by the molecular shape of ions, morphology of the micropores and applied voltages is largely lacking. In this work, we combined molecular dynamics simulations with enhanced sampling methods to elucidate the mechanism of nitrate and chloride selectivity in subnanometer graphene slit-pores. We demonstrate that nitrate is preferentially adsorbed over chloride in the slit-like micropores. This preferential adsorption was found to stem from the weaker hydration energy and unique anisotropy of the ion solvation of nitrate. Beside the effects of ion dehydration, we found that applied potential plays an important role in determining the ion selectivity, leading to a lower selectivity of nitrate over chloride at a high applied potential. We conclude that the measured ion selectivity results from a complex interplay between voltage, confinement, and specific ion effects-including ion shape and local hydration structure.
The direct synthesis of H 2 O 2 from O 2 and H 2 provides a green pathway to produce H 2 O 2 , a popular industrial oxidant. Here, in this study, we theoretically investigate the effects of Pd oxidation states, coordination environments, and particle sizes on primary H 2 O 2 selectivities, assessed by calculating the ratio of rate constants for the formation of H 2 O 2 (via OOH* reduction; k O–H ) and the decomposition of OOH* (via O–O cleavage; k O–O ). For Pd metals, the k O–H /k O–O ratio decreased from 10 -4 for Pd(111) to 10 -10 for the Pd 13 cluster at 300 K, indicating poorer H 2 O 2 selectivity as Pd particle size decreases and low primary selectivities for H 2 O 2 overall. As the oxygen chemical potential increases and metals form surface and bulk oxides, the perturbation of Pd–Pd ensemble sites by lattice O atoms results in selectivities that become dramatically higher than unity. For instance, at 300 K, the k O–H /k O–O ratio increases significantly from 10 -4 to 10 9 to 10 16 as Pd(111) oxidizes to Pd 5 O 4 /Pd(111) and to PdO(100), respectively. In contrast, such selectivity enhancements are not observed for surface and bulk oxides that persistently contain rows of more metallic, undercoordinated Pd–Pd ensemble sites, such as PdO(101)/Pd(100) and PdO(101). These Pd–Pd ensembles are also absent when smaller Pd nanoparticles fully oxidize, indicating that smaller PdO clusters can be more selective for H 2 O 2 synthesis. These trends for primary H 2 O 2 selectivities were found to inversely correlate with trends for H 2 O 2 decomposition rates via O–O bond cleavage, demonstrating that catalysts with high primary H 2 O 2 selectivity can also hinder H 2 O 2 decomposition. Ab initio thermodynamic calculations are used to estimate the thermodynamically favored phase among Pd, PdO/Pd and PdO in O 2 , H 2 O 2 /H 2 O, and O 2 /H 2 environments. These results are combined to show that smaller Pd nanoparticles are more prone to be oxidized at lower oxygen chemical potentials, upon which they become more selective than larger Pd particles for H 2 O 2 synthesis.
Abstract Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.
Genetic mutants defective in stimulus-induced Ca 2+ increases have been gradually isolated, allowing the identification of cell-surface sensors/receptors, such as the osmosensor OSCA1. However, determining the Ca 2+ -signaling specificity to various stimuli in these mutants remains a challenge. For instance, less is known about the exact selectivity between osmotic and ionic stresses in the osca1 mutant. Here, we have developed a method to distinguish the osmotic and ionic effects by analyzing Ca 2+ increases, and demonstrated that osca1 is impaired primarily in Ca 2+ increases induced by the osmotic but not ionic stress. We recorded Ca 2+ increases induced by sorbitol (osmotic effect, OE) and NaCl/CaCl 2 (OE + ionic effect, IE) in Arabidopsis wild-type and osca1 seedlings. Here we assumed the NaCl/CaCl 2 total effect (TE) = OE + IE, then developed procedures for Ca 2+ imaging, image analysis and mathematic fitting/modeling, and found osca1 defects mainly in OE. The osmotic specificity of osca1 suggests that osmotic and ionic perceptions are independent. The precise estimation of these two stress effects is applicable not only to new Ca 2+ -signaling mutants with distinct stimulus specificity but also the complex Ca 2+ signaling crosstalk among multiple concurrent stresses that occur naturally, and will enable us to specifically fine tune multiple signal pathways to improve crop yields.
The ninterp crate provides multivariate interpolation over a regular, sorted, nonrepeating grid of any dimensionality. A variety of interpolation strategies are implemented, however more are likely to be added. Extrapolation beyond the range of the supplied coordinates is supported for 1-D linear interpolators, using the slope of the nearby points. There are hard-coded interpolators for lower dimensionalities (up to N = 3) for better runtime performance. All interpolation is handled through instances of the Interpolator enum, with the selected tuple variant containing relevant data. Interpolation is executed by calling Interpolator::interpolate.
Nanostructured Cu catalysts have increased the selectivities and geometric activities for high value C–C coupled (C 2 ) products (ethylene, ethanol, and acetate) in the electrochemical CO (2) reduction reaction (CO (2) RR). The selectivity among the high-value C 2 products is also altered, where for instance the yield of acetate increases with alkalinity and is dependent on the catalyst morphology. The reaction mechanisms behind the selectivity towards acetate vs. other C 2 products remain controversial. In this work, we elucidate the reaction mechanism for acetate formation by using ab initio simulations, a coupled kinetic-transport model, and loading dependent experiments. We find that trends in acetate selectivity can be rationalized from variations in electrolyte pH and the local mass transport properties of the catalyst and not from changes in Cu's intrinsic activity. The selectivity mechanism originates from the transport of ketene, a stable (closed shell) intermediate, away from the catalyst surface into solution where it reacts to form acetate. While this type of mechanism has not yet been discussed in the CO (2) RR, variants of it may explain similar selectivity fluctuations observed for other stable intermediates like CO and acetaldehyde. Our proposed mechanism suggests that acetate selectivity increases with increasing pH, decreasing catalyst roughness and significantly varies with the applied potential.
Due to the limited uranium resources available for recovery through mining and ore processing and to the environmental problems associated with these operations, the future of nuclear power depends, to a large extent, on the feasibility of recovery of uranium from the ocean. While the amounts of uranium in the ocean are sufficient to fulfill the nuclear industry for many thousands of years, recovery of uranium from the ocean is hampered by its low concentration (3.3 parts per billion) and the presence of many other solutes in seawater at comparable or much higher concentrations. Thus, the only hope for economically viable recovery of uranium from the ocean lies in the development of adsorbents with high capacity and high selectivity for uranium as present in the seawater environment (pH 8.1, U(VI) oxidation state, anionic carbonate complexes). Moreover, it is quite unlikely that economically viable recovery of uranium could be achieved if the adsorbent could be used only once. Thus it is essential to develop adsorbents that can be used through multiple cycles of uranium adsorption followed by elution of the uranium to regenerate the adsorbent without significant degradation of their capacity and selectivity for uranium. Adsorbents used in the large majority of studies aimed at recovery of uranium from seawater are based on the use of radiation grafting to produce polymeric supports, in particular polyethylene, with attached amidoxime groups. This configuration has been concluded to offer the best candidate for use in recovery based on studies which were performed in the early 1980’s. Substantial progress has been made in improving the amidoxime-based adsorbents. The basic approach undertaken by the authors of the proposal, on the other hand, has been to test ligands which have not been included in the scoping studies of the 1980’s or have been developed since then. So far, 18 such ligands have been tested, of which three, including B2MP (an organic phosphate), DAOx (an organic oxalate), and Br-PADAP (an azo compound) have been selected for further investigation. In addition, Winged Nylon 6 has been identified as a promising polymeric support in view of its large surface area and high chemical stability. Furthermore, improved grafting techniques intended to achieve high grafting density under “green chemistry” conditions, i.e., in aqueous media, have been developed. These techniques include the use of surfactants to enhance the solubility of ligands in water and the use of a homopolymerization inhibitor to promote grafting rather than homopolymerization. Furthermore, unlike amidoxime-based adsorbents, which require a chemical treatment step following the radiation grafting to convert a nitrile group to amidoxime, the adsorbents developed by the authors do not require an additional step after grafting. Highly effective regeneration has been demonstrated for the phosphate-grafted polymeric adsorbent over at least 21 adsorption/elution cycles without significant loss of capacity. The currently proposed study is intended to build upon the progress made so far in developing alternative systems for removal of uranium from seawater. It will focus on the following objectives: Continued searches will be conducted for high-capacity, high-selectivity ligands based on systematic characterization of structural features favorable to uranium adsorption. (For instance, phosphates have been found to be much more effective than the corresponding phosphonates). At the same time, development and testing of the three ligands selected so far will continue, focusing on issues such as high grafting densities and effective regeneration. Testing will be conducted in non-spiked sweater (3.3 µg/L U) and lightly spiked seawater (up to 10 µg/L U). The authors have recently been able to refurbish an ICP-MS spectrometer for this purpose. Improved grafting techniques will be developed and refined for specific ligands. These techniques will include the use of surfactants to allow grafting of ligands with low solubility in water, the use of homopolymerization inhibitors, modifying the chemical structure of ligands to introduce C=C bonds, investigating possible modification of the surface of the polymeric support, and mechanistic analysis of the effects of grafting conditions, in particular accumulated dose and dose rate. Improvements in the characterization of adsorbents before and after contact with seawater will be implemented using techniques such as XPS, SEM-EDS, and zeta potential measurements to investigate the mechanism of uranium adsorption. Effective techniques of multi-cycle regeneration of promising adsorbents without degradation of their performance will be developed and refined through testing of mild, preferably near-neutral eluant solutions. The kinetics of adsorption and desorption of uranium will be investigated. This study is of great operational value and it will also greatly contribute to improved understanding of adsorption/desorption mechanism on promising adsorbents.
Dynamics are intricately linked with activity and selectivity when it comes to catalysis, as noted for instance in the enzymatic principles of induced fit and allostery, and yet the range of motions heterogeneous catalytic sites are able to undergo is poorly understood. Solid-state nuclear magnetic resonance (NMR) spectroscopy is perhaps the only tool capable of probing the rapid conformational dynamics found in heterogeneous catalysts but has historically been restricted by its low sensitivity, limiting the detail with which structures can be resolved. In this work, we apply solid-state NMR and dynamic nuclear polarization, in combination with density functional theory modeling, to reveal the high-resolution structure and motional freedom of a scandium supported complex in three dimensions. The results are contrasted with the study of the analogous homoleptic complex in the crystalline state, highlighting the impacts that surface structure may have on the dynamics of supported complexes.
Daft is a Haskell library that implements the key constructs of relational algebra via static structural typing. Its DataCube typeclass supports selection, projection, aggregation, disaggregation, natural joins, semijoins, and antijoins. Instances of DataCube include both map- and function-based representations. An additional abstract data type provides Vinyl-style, keyed relational tables for which an embedded domain-specific language compactly represents relational operations. Utility types and functions support serialization to tab-separated-value data files, JSON arrays, MongoDB documents, and PostgreSQL tables, including automatic detection and verification of the schema for the input data.
A key theme of heterogeneous catalysis research is achieving control of the environment surrounding the active site to precisely steer the reactivity toward desired reaction products. One method toward this goal has been the use of organic ligands or self-assembled monolayers (SAMs) on metal nanoparticles. Metal-bound SAMs are typically employed to improve catalyst selectivity but often decrease the reaction rate as a result of site blocking from the ligands. Recently, the use of metal oxide-bound organic modifiers such as organophosphonic acid (PA) SAMs has shown promise as an additional method for tuning reactions on metal oxide surfaces as well as modifying oxide-supported metal catalysts. In this Account, we summarize recent approaches to enhance catalyst performance with oxide-bound monolayers. These approaches include (1) modification of metal oxide catalysts to tune surface reactions, (2) formation of SAMs on the oxide component of supported metal catalysts to modify sites at the metal–support interface, and (3) enhancement of catalyst performance (e.g., stability) through modification of sites remote from the active sites. Further, both the headgroups and organic tail groups of PA SAMs or other ligands can influence reactions on metal oxide surfaces. Binding of the headgroup can selectively poison certain active sites, altering the selectivity in a manner analogous to metal-bound ligands (at the expense of active site quantity). Moreover, tail groups can be functionalized to interact favorably with reactants and intermediates, for instance through dipole–dipole interactions. On supported metal catalysts like Pt/Al 2 O 3 , PA SAMs can selectively form on the oxide support. This selective deposition allows for modification of the metal–support interface with minimal blockage of metal sites. PA headgroups were shown to provide tunable acid sites at the interface, dramatically improving hydrodeoxygenation rates of various alcohols. Additionally, organic tail functionality was used to activate or stabilize specific reactants at the interface, such as with the use of amine-functionalized PAs to stabilize chemisorption of CO 2 during the reverse water gas shift reaction. PAs have also been found to affect the electronic properties of bulk metal sites through long-range electron withdrawal via the oxide, providing an additional avenue to tune catalytic behavior. Finally, organic modifiers were shown to enhance catalytic performance without directly modifying the active site. For instance, in biphasic liquid environments the modification of catalyst particles with hydrophobic or hydrophilic SAMs shifts the selectivity of multipath reactions on the basis of the hydrophobicities of different intermediates and products. As another “long-range” effect, the deposition of ligands on oxide supports improved catalyst stability through both improved resistance to sintering and suppression of active site poisoning. The recent contributions discussed in this Account demonstrate the versatility and significant potential for the approach of modifying catalysts with oxide-bound organic monolayers.
A machine learning–based framework for modeling the error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (eg, random forests, and LASSO) to map a large set of inexpensively computed “error indicators” (ie, features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering) and subsequently constructs a “local” regression model to predict the time-instantaneous error within each identified region of feature space. We consider 2 uses for the resulting error model: (1) as a correction to the surrogate-model QoI prediction at each time instance and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model error (eg, time-integrated errors). We then apply the proposed framework to model errors in reduced-order models of nonlinear oil-water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. Moreover, when the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged errors.
Selectivity is a critical attribute of catalysts used in manufacturing of essential and fine chemicals. An excellent way to induce selectivity in catalysts is by using ultrathin films with tailored nanoporosity. For instance, nanopores can be created in atomic layer deposition (ALD) ultrathin over-coatings on supported metal nanoparticles by subjecting the coatings to high temperature annealing. These nanopores expose the active surface of the underlying metal nanoparticles. The dimensions of these nanopores can be tuned to impart shape selectivity: only reactants or products with a specific size or shape can fit inside the pore. In this work, the authors explore the underlying mechanism driving nanopore formation in ALD films. Ultrathin films of ALD TiO 2 (similar to 2.5 nm thick) and ALD Al 2 O 3 (similar to 4.9 nm thick) were deposited on nonporous gamma-Al 2 O 3 nanoparticles. The pore formation and evolution were monitored in situ during thermal annealing using small-angle x-ray scattering (SAXS), and the crystallinity was monitored by in situ x-ray diffraction. A correlation between the nanopore formation and amorphous to crystalline phase transitions in the ALD layers was observed. The authors hypothesize that the pores form through the relaxation of stress induced by densification of the ALD films during the phase transitions. In conclusion, the authors developed a mathematical model to evaluate this hypothesis and found remarkable agreement between the model and the SAXS measurements.
Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.
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Silver molecules are chromophores with diverse spectra and rich photophysics, and DNA strands act as ligands that develop specific molecular silver species. For instance, C 4 AC 4 T*C 3 GT 4 selectively yields a Ag 10 6+ fluorophore with λ ex /λ em = 490/550 nm. This single-stranded DNA coordinates and protects the cluster, and its integrity was challenged by breaking the phosphodiester backbone at the innocuous T*. The resulting C 4 AC 4 T and C 3 GT 4 fragments also develop the same Ag 10 6+ fluorophore but only when all three components (two fragments + Ag) are present. This C 4 AC 4 T/C 3 GT 4 /Ag 10 6+ complex is favored by higher DNA concentrations and preferentially forms when hybridization forces C 4 AC 4 T and C 3 GT 4 onto a shared DNA duplex. The C 4 AC 4 T/C 3 GT 4 assembly reverses when the cluster photodegrades, which suggests that the cluster is labile. C 4 AC 4 T forms an alternate cluster at low temperatures, but C 3 GT 4 recovers the λ = 490 nm fluorophore at higher temperatures. Thus, the reciprocal interactions between the host DNA strands and the cluster adduct combine to create a highly specific, split-DNA fluorescent Ag cluster capable of sensing target DNA sequences with high specificity and on–off contrast.
The microscopic origins of emergent behaviours in condensed matter systems are encoded in their excitations. In ordinary magnetic materials, single spin-flips give rise to collective dipolar magnetic excitations called magnons. Likewise, multiple spin-flips can give rise to multipolar magnetic excitations in magnetic materials with spin S ≥ 1. Unfortunately, since most experimental probes are governed by dipolar selection rules, collective multipolar excitations have generally remained elusive. For instance, only dipolar magnetic excitations have been observed in isotropic S = 1 Haldane spin systems. Here, we unveil a hidden quadrupolar constituent of the spin dynamics in antiferromagnetic S = 1 Haldane chain material Y 2 BaNiO 5 using Ni L 3 -edge resonant inelastic x-ray scattering. Our results demonstrate that pure quadrupolar magnetic excitations can be probed without direct interactions with dipolar excitations or anisotropic perturbations. Originating from on-site double spin-flip processes, the quadrupolar magnetic excitations in Y 2 BaNiO 5 show a remarkable dual nature of collective dispersion. While one component propagates as non-interacting entities, the other behaves as a bound quadrupolar magnetic wave. This result highlights the rich and largely unexplored physics of higher-order magnetic excitations.
ABSTRACT Although biocatalytic transformation has shown great promise in chemical synthesis, there remain significant challenges in controlling high selectivity without the formation of undesirable by-products. For instance, few attempts to construct biocatalysts for de novo synthesis of pure flavin mononucleotide (FMN) have been successful, due to riboflavin (RF) accumulating in the cytoplasm and being secreted with FMN. To address this problem, we show here a novel biosynthesis strategy, compartmentalizing the final FMN biosynthesis step in the periplasm of an engineered Escherichia coli strain. This construct is able to overproduce FMN with high specificity (92.4% of total excreted flavins). Such a biosynthesis approach allows isolation of the final biosynthesis step from the cytoplasm to eliminate undesirable byproducts, providing a new route to develop biocatalysts for the synthesis of highpurity chemicals. IMPORTANCE The periplasm of Gram-negative bacterial hosts is engineered to compartmentalize the final biosynthesis step from the cytoplasm. This strategy is promising for the overproduction of high-value products with high specificity. We demonstrate the successful implementation of this strategy in microbial production of highly pure FMN.